Despite the public debate between Elon Musk and Mark Zuckerberg on whether or not artificial intelligence (AI) is good for humanity and on the heels of Facebook's news it had shut down its AI engine because chatbots had started talking to one another in their own language, AI is still in high-growth mode. Analyst firm Research and Markets predicts the global AI market to grow to $23.4 billion by 2025.

In July 2017, UK startup, Graphcore, closed a $30 M investment round for Niklas Zennstrom's venture capital firm, Atomico. Zennstrom was one of the co-founders of Skype. Graphcore makes chips specifically for AI applications. Other investors in this funding round include Draper Esprit, Robert Bosch Venture Capital, Amadeus Capital, C4 Ventures, Foundation Capital, Pitango and corporate investors Dell Technologies Capital and Samsung Catalyst Fund. Individual investors include Demis Hassabis, the CEO of DeepMind. The total investment for the company is $62 M.

Graphcore's investment comes on the heels two recent global AI milestones. The first is China's recent announcement of their plans to be the world's AI leader by 2030 and a New York Times (paywall) story that China's top ranked games player, had been beaten in the strategy game of Go by AlphaGo, a board game program from Alphabet's DeepMind. DeepMind focuses on AI research and applications for a positive impact in multiple market sectors.

Luke Tang, General Manager of TechCode, a global accelerator for AI startups, says that AI is now synonymous with innovation.

"The idea people have is that you can apply AI and machine learning to any technology and you’ll have something revolutionary. At the fundamental level though, every AI-powered technology is dependent upon a chip processor. While AI companies are a dime a dozen, currently there aren’t many chip companies with products capable of handling the intensive computing of AI," said Tang. "With a lack of options within the chip industry, we’ll start to see tech giants and investors alike look more closely at chip makers that can not only bring AI beyond its current stage but also support AI technology into the future."

Tang says that previous chips, like Intel’s CPU, aren't suitable because they are mostly designed for serial processing and machine learning is essentially parallel computing.

"NVIDIA’s chips are designed for parallel computing – previously for gaming and imaging processing – and therefore companies are switching over to NVIDIA’s GPU to accelerate deep learning applications," said Tang. "Look no further than the skyrocketing price of NVIDIA’s stock, to see the evidence of how the chip industry is influenced by the AI trends. But even NVIDIA’s GPU isn’t good enough since it was not purposely built for machine learning and thus restricted by its legacy architecture."

Tang believes a new generation of chip, specifically designed for the machine learning era, is critically needed to handle AI problems that are more difficult and data intensive in the future.

"Graphcore is the new player on the scene trying to tackle this big challenge, designing chips to run machine learning at least 100x faster with much lower cost. I’m confident that companies like Intel are going to keep a close eye on Graphcore’s success," said Tang. "If in fact, it can live up to its hype, we are going to see tech giants fight to acquire it since there are not many options available. If it’s successful, it will generate another big push to the adoption of AI across industry verticals for many years to come.

Back to the future

Generationally speaking, the Institute of Electrical and Electronics Engineers (IEEE) conducted a survey in July 2017 that looked at the sentiment of Millenials and raising their kids, known as Generation Alpha, alongside technology and AI. Some of the most telling data showed that 63 percent of the Millenials surveyed said they would prefer that AI helps them live independently in their golden years with 37 percent who would choose to rely on their own children. Eighty percent of Millenials said that their children would learn faster than they did at the same age because of AI.

With governmental-backed funding for science in question, incubators and accelerators like TechCode and MassChallenge are picking up the pieces for AI and machine learning startups.

MassChallenge in Boston recently announced their 2017 class which has a two-fold increase in machine learning and AI start ups. Out of the 128 startups, 30 percent are healthcare and life sciences while only six percent are cleantech and energy.

"Every year, we see an increase in the number of startups that apply to our accelerator program to launch and grow their machine learning technologies," said Kiki Mills Johnston, Managing Director, MassChallenge Boston. "As our society generates more and more data across industries, young, nimble companies have stepped up to play a critical role in making sense of those insights and extracting real value. Today, startups are driving machine learning adoption and doing amazing things along the way, like identifying fresh water sources, improving visual communications, and even modernizing disease diagnostics."

Machine learning startups such as Upstream Tech are one of those clean tech six percent. They are using machine learning and satellite imagery to monitor and measure freshwater resources from space. Their goal is to enable freshwater conservation with smart water management technology.

"Water markets have extraordinarily high transaction costs, which leads to low participation," said Marshall Moutenot, Co-Founder, Upstream. "We hope that by decreasing these costs, we can drive participation, reallocate water to high economic values, and economically incentivize conservation."

Moutenot says natural river habitats and freshwater species aren't allocated enough water to thrive.

"One-way conservation organizations combat this is by buying or leasing water rights, which would otherwise be used for agriculture, in-stream," adds Moutenot. "The process of identifying water rights to reallocate, monitoring them for compliance and tracking the positive impacts increased flow has on the environment are currently all manual processes involving in-person visits, expensive sensors and time consuming geospatial work, we've automated the processes of monitoring and measuring fresh water resources using machine learning in concert with satellite imagery, starting by helping conservation organizations with their most costly processes."

In life sciences, Day Zero Diagnostics is combining whole genome sequencing and machine learning to speed up the diagnosis and rapidly identify the strain and antibiotic resistance profile of a bacterial infection within hours.

Day Zero Diagnostics hopes to change the way physicians triage patients with severe infections and make the large scale generation of genomic data by hospital microbiology labs routine that can then be used to change clinical decision making.

"We are developing a diagnostic that can identify a bacterial infection and provide its antibiotic susceptibility profile in hours rather than days. We use high throughput sequencing technologies to sequence the genomes of the pathogens, and then interpret the genomic data using our machine learning algorithms," said Jong Lee, Founder, Day Zero Diagnostics. "Our machine learning approach allows us to predict drug susceptibility even in cases where the mechanism of resistance may not be fully understood or recognized. Our algorithms are trained on a large and constantly growing database of pathogens, and have already demonstrated an ability to accurately and comprehensively identify pathogen species and predict drug resistance profile."

Texas-based KinTrans is taking the education route with AI. This MassChallenge startup is using machine learning tech to deliver intuitive, real-time sign language translation that opens new opportunities for companies who employ and service deaf people.

"Sign language is an example of important body movements used by deaf people to communicate. Other attempts to recognize sign language automatically have not reached intended results due in large part to the fact that they tried to recognize sign language itself, not the movement," said Bentley. "Our patent-pending machine learning engine thinks about the movement data it receives from a 3D depth camera, identifies movement models and places the associated meanings."

I'm a writer who looks at innovation and how technology and science intersect with industry, arts, agriculture, mobility, health. I've been called the tech Hemingway of Paris, named one of the top 100 women in technology in Europe in 2012, short-listed for best tech journali...